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FastAPI is a modern, fast (high-performance), web framework for building APIs with Python. Serving a model with FastAPI is the easiest way to deploy a model. We basically wrap our inference function in a FastAPI app. Here we will work with a very simple scikit-learn iris classification model. You can find the FastAPI code for this model here.
Live DemoYou can view this example deployed here.
They key files are:
  • iris_classifier.joblib: The model file
  • server.py: The main FastAPI code that loads the model and provides a REST api to serve the model.
  • requirements.txt: Contains the dependencies.

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How to write the inference function in FastAPI

Here’s an explanation of the code in server.py 
import os
from contextlib import asynccontextmanager
from typing import Dict

import joblib
import pandas as pd
from fastapi import FastAPI

def _get_model_dir():
    if "MODEL_DIR" not in os.environ:
        raise Exception(
            "MODEL_DIR environment variable is not set. Please set it to the directory containing the model."
        )
    return os.environ["MODEL_DIR"]


model = None
MODEL_PATH = os.path.join(_get_model_dir(), "iris_classifier.joblib")

def load_model():
    _model = joblib.load(MODEL_PATH)
    return _model


# Here we are loading the model in global variable model using 
# FastAPI's lifespan context manager (https://fastapi.tiangolo.com/advanced/events/#lifespan-events).
# This makes sure we load the model before server is ready to serve requests.
@asynccontextmanager
async def lifespan(app: FastAPI):
    global model
    model = load_model()
    yield


app = FastAPI(lifespan=lifespan, root_path=os.getenv("TFY_SERVICE_ROOT_PATH", ""))

# Add a health check endpoint which will be used for readiness and liveness 
# probe in the deployment
@app.get("/health")
async def health() -> Dict[str, bool]:
    return {"healthy": True}

# This is the main prediction endpoint that is used to serve the model
@app.post("/predict")
def predict(
    sepal_length: float, sepal_width: float, petal_length: float, petal_width: float
):
    global model
    class_names = ["setosa", "versicolor", "virginica"]
    data = dict(
        sepal_length=sepal_length,
        sepal_width=sepal_width,
        petal_length=petal_length,
        petal_width=petal_width,
    )
    prediction = model.predict_proba(pd.DataFrame([data]))[0]
    predictions = []
    for label, confidence in zip(class_names, prediction):
        predictions.append({"label": label, "score": confidence})
    return {"predictions": predictions}
Load the file from the path specified in the MODEL_DIR environment variableWe are using an environment variable MODEL_DIR to read the model path. This is useful when we want to load a model from a different path that can be downloaded and cached separately. See Cache Models and Artifacts guide for more details.
async def vs defFastAPI is designed to be an async web framework. This means that the server can handle multiple requests concurrently. When using async def, it is the developer’s responsibility to make sure the function is not blocking the event loop. However, most ML Inferences are compute bound and block the event loop. Use def instead of async def for inference functions.Generally, if you are not sure just use def. FastAPI (mainly Startlette which is the foundation of FastAPI) automatically runs the sync functions in a thread pool to not block the event loop.

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Running the server locally

Shell gunicorn
export MODEL_DIR="$(pwd)"
gunicorn -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000 server:app
We’ll see something like this: 
INFO:     Started server process [53896]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
We can open the browser and navigate to http://localhost:8000/docs to try out our API.

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Deploying with TrueFoundry

To deploy the model, we need to package both the model file and the code. To do this, we can follow the steps below:
1

Log the Model To Model Registry

Logging the model to the registry is not mandatory to deploy the model, but is highly recommended. You can follow the guide here to log the model to the registry.
2

Push the code to a Git repository or directly deploy from local machine

Once you have tested your code locally, we highly recommend pushing the code a Git repository. This allows you to version control the code and also makes the deployment process much easier. However, if you don’t have access to a Git repository, or the Git repositories are not integrated with Truefoundry, you can directly deploy from local laptop.You can follow the guide here to deploy your code. A few key things to note:
Binding to 0.0.0.0 in commandPlease make sure you put --bind 0.0.0.0:8000 in the command. By default, gunicorn binds to 127.0.0.1. To make it accessible from outside the environment, we need to bind to 0.0.0.0.
The port number in the ports section should match the port your model server will listen on. E.g. In this case gunicorn is being told to bind on 8000 in command, hence we use 8000 in ports
3

Download Model from Model Registry in the deployment configuration

If you logged the model to the registry in Step 1, TrueFoundry can automatically download the model at the path specified in the MODEL_DIR environment variable to the deployed service. To enable this, you can modify the deployment configuration as follows:Download Model from Model Registry
4

View the deployment, logs and metrics

Once the deployment goes through, you can view the deployment, the pods, logs, metrics and events to debug any issues.

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FAQ

The best way is to start with something high - like 1 CPU, 2 GB RAM. Once the service is up, you can see the resource usage from the metrics section and then adjust the resources accordingly.
We usually recommend to use 1 Gunicorn worker and instead use replicas field to scale the number of containers. This has several benefits:
  • Stable CPU and Memory usage across replicas
  • Even distribution of requests across replicas
  • Higher availability and fault tolerance
We can also go ahead with 2 workers and 2 CPU (1 for each worker), but higher number of workers is not usually recommended, unless you are sure that is needed.
FastAPI / Starlette uses asyncio to handle requests concurrently. Starlette’s ThreadPool count is set to 40 by default. While this is useful to run many requests concurrently, ML Inference is compute bound, if our resources are not well tuned, we might get CPU Throttling or OOM.
This should be done only after careful consideration.Don’t set this to too low. These threads are also used for FastAPI’s internal co-routines.
We can tune the number of threads like so:
import anyio.to_thread

limiter = anyio.to_thread.current_default_thread_limiter()
limiter.total_tokens = 10
Generally when working with ML Inference and FastAPI, avoid using gevent or eventlet as they are not compatible with asyncio. Several machine learning libraries also use ThreadPools and ProcessPools which can cause issues with gevent or eventlet.
Yes, you can deploy the same service using Flask + Gunicorn. However, we generally recommend using FastAPI as it is more modern and has better support for asyncio. In case you are using Flask:
  • Use Gunicorn as the application server
  • Use Gunicorn’s sync worker class (it is the default worker class) instead of gevent or eventlet
  • Use Gunicorn’s --workers argument to increase parallelism per container.
    • Although, don’t set this to too high. We generally recommend setting it to 2 so that health check and inference can run concurrently. For scaling beyond that, use Service replicas.
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